Case study

€13,200 in 2 months, from content that was already working

By the time ConfiguraThor launched in October 2025, my YouTube tutorials had been bringing in leads for a year. This is how that demand got built.

Scope
Growth, distribution and sales
Period
Jun 2022 to Aug 2026
Stack
YouTube, WordPress, Search Console, Google Analytics, Microsoft Clarity, Apify, Apollo, Lusha, Instantly, Smartlead, Lemlist, Claude Code, Cloudflare Workers
€13,200
Closed in the 2 months after launch
3 a month
Inbound leads worth €1,000+ each, from YouTube
2,010+
AI citations in a 28-day window
5 months
From the first tutorials to the first leads
The problem

Nobody knew Eyedex existed

Eyedex started in 2022 as a side project, selling augmented reality to e-commerce stores. No audience, no leads, no brand, no list to email.

Cold outreach was the obvious move and it was also the one that ran out fastest. Every deal needed the same energy as the one before it. So I went the other way and built something that keeps working while I sleep.

In May 2024 I got my first client for a 3D configurator, and that became the focus. In 2025 I turned the work into a product, ConfiguraThor. That page is about building it. This one is about getting it found.

What I did

I stopped selling and started answering

I answered what e-commerce owners actually search

From 2024 I recorded YouTube tutorials on 3D configurators and AR experiences: how they work, the mechanics behind them, and where they fit in the sales funnel of a store. Built around the questions people type when they try to solve the problem themselves.

It took about 5 months before they brought in a lead. From autumn 2024 they brought in about 3 high-ticket leads a month, each worth €1,000 or more.

Same intent, three surfaces

Google, YouTube and the LLMs. Same buyer, same intent. I restructured the content so answer engines could extract it, added structured data, and tracked citations across ChatGPT, Perplexity and Google AI Overviews.

In one 28-day window, LLMs cited me 2,010+ times. Every one of those is a person asking how to solve their problem and getting my name in the reply, before they have ever seen my site.

I got the pricing wrong twice

Two of the prices I set were wrong, and I did not find that out in a spreadsheet. I found it out in conversations with buyers, in the pause after I said the number.

Changing it twice cost me time. It also gave me the one thing the competition did not have: a price a small brand can approve without a committee, with no subscription attached.

I published the case studies and distributed them

Every finished build became a story with numbers in it, published and pushed on LinkedIn, so the proof was findable long after the project ended.

The outbound side

I built the tooling myself

Content was the main engine. Next to it I built the outbound and lead tooling in-house, with Python, LLMs and Claude Code.

Lead scraping and enrichment

Pipelines on Apify with LLM-based lead classification and email enrichment, writing qualified prospects straight into Google Sheets. Apollo and Lusha filled in the rest.

Cold email

Campaigns on Instantly, Smartlead and Lemlist, fed by those lists. For a niche, high-ticket product like a configurator the replies stayed low, which is part of why search and video carried the growth.

An intent radar

A monitor on Reddit, Upwork and job boards that picks up buying signals, scores each lead and sends the best matches to Slack.

Free tools

A search and AI visibility checker with a paid report tier, a website conversion audit and a content gap finder, built on Cloudflare Workers with Claude Code. Try the free tools.

Outside Eyedex: same pipeline, another market

I pointed the same scraping and personalization at a different market: cold email offering SEO services to clinics in Italy. That campaign got a 14% reply rate. Same tooling, a market where the offer was easier to say yes to.

The search side is the same sequence I run for clients under the name OCAVI: find where buyers are searching, look at where competitors already win, work out where you can realistically compete, then build the pages and the videos that get you found and cited. Neuragate is where I learned it. Eyedex is where I ran it on my own money, in the AI era, on a market that did not know I existed.

What it produced

The numbers, with nothing rounded up

€13,200
Closed in the 2 months after ConfiguraThor launched in October 2025, with no ad budget behind it.
First sale in 2 weeks
The first sale came inside 2 weeks of launch.
3 leads a month
Inbound, high-ticket (€1,000 and up), from YouTube, every month from autumn 2024.
2,010+ AI citations
In one 28-day window, that is how many times an LLM pulled me into an answer for someone else.
Five figures from older content
The deals in late 2025 came from people watching the new tutorials and the ones I had recorded more than a year earlier.

The tutorials were the part that compounds. They were recorded once, they kept bringing qualified leads every month, and they cost nothing to keep running. That is the difference between buying attention and owning the place where people look for it.

Transferable

What I would take into your business

I find out what your buyers type before I write anything. Not what your team calls the product, what a person with the problem searches at 11pm.

I put the product on video where the searching happens. A tutorial that solves a real problem is a sales asset that keeps working more than a year later.

I treat pricing as something you test with buyers, not something you decide alone. I got mine wrong twice and only found out on calls.

I build the growth tooling myself when that is faster: scrapers, enrichment, alerts, small tools. And I say so when a channel is not working, instead of feeding it more volume.

I check whether AI cites you, not only whether Google ranks you. Being the answer someone reads matters more than being the tenth blue link.

I did this with no audience. You are probably starting from more

If your buyers are searching and finding someone else, that gap is fixable and it is mostly research before it is writing. Three months to install the system, then it keeps working on its own.

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